Publication
Gene regulatory networks control gene expression levels, and therefore play an essential role in mammalian development and function. Regulation of gene expression is the result of a complex interplay between DNA regulatory elements and their binding partners, known as transcription factors (TFs). Due to their vital role in development, intercellular signalling, cell cycle and disease development, elucidating the mechanisms by which TFs regulate gene expression is of crucial importance in the vast majority of biological processes. In particular, understanding how each TF contributes to the expression output of its respective target gene in space and time will help to elucidate how gene regulatory networks (GRNs) behave under different physiological or pathological conditions. Although extensive work has been accomplished in characterizing the key TFs involved in many biological processes, almost no quantitative information is currently available in the literature. To get a deep insight into the complex mechanisms underlying the regulation of gene expression, we need to acquire quantitative information, since TF abundance within the cell can be linked to their transcriptional capabilities. Such information would be of utmost importance to build accurate in silico quantitative DNA binding models that could predict and explain the particular properties of gene regulatory mechanisms. The quantification of TFs is a difficult task due their natural low abundance in cells, and their reliable detection is therefore very much dependent on the overall sensitivity of current technologies. In recent years, a new MS-based technology termed selected reaction monitoring (SRM) has gained popularity due to the targeted nature of its approach that allows the detection and quantification of proteins in complex samples with an exceptional sensitivity and specificity. I will show in this thesis, this approach is particularly well suited for targeting low abundant proteins such as TFs, which are otherwise difficult to identify with conventional shotgun proteomics experiments. Consequently, the main focus of my thesis research project entailed the development of an SRM-based platform aimed at quantifying TFs in absolute amounts based on in vitro protein expression during the terminal stage of adipogenesis, using the pre-adipocyte 3T3-L1 cell line. Interestingly, our initial efforts led to the creation of an atlas of TF-specific peptide data, which could be readily used for the design of quantitative assays. In the first phase, abundance measurements in terms of copies per cell were derived at precise differentiation time-points for two major adipogenic players, PPARγ and RXRα. In the second phase, we expanded the number of adipogenic TFs that can be monitored in one assay, allowing for the quantification of up to 10 TFs in one single, integrated SRM run. Such upscale increases the practical usefulness of the methodology while reducing the associated costs, and ultimatel